The Impact of Real-World Data and Cloud Technology on Clinical Research and Health Outcomes

Dr. Lu de Souza | April 2024

Far too often, valuable data sources in the healthcare and life sciences sectors are siloed, disparate, and unusable. However, when unified, put into research-ready formats, and enhanced by AI and other cloud-based technologies, they can drive significant breakthroughs. These range from speeding up drug discovery and diversifying clinical trial participants to advancing precision medicine. The use of real-world data (RWD) and real-world evidence (RWE) marks a new frontier in advancing healthcare and life sciences.

Oracle Learning Health Network, where I serve as vice president and executive medical officer, is a vast resource of RWD, with its extensive repository of 108 million active longitudinal records (and growing), sourced from an array of healthcare settings including rural community hospitals and metropolitan medical centers. A membership community, the Learning Health Network consists of 117 health systems representing 2,600 facilities across the United States. We’re expanding globally in 2024.

These health systems are acting for the greater good of humanity, choosing to collaborate with each other and companies such as Oracle on initiatives that can bring transformative change to healthcare. The de-identified data managed by the Learning Health Network isn’t just voluminous, but also rich in its diversity, helping members advance clinical trial and outcomes research for all communities.

Improving clinical trials

We need a greater number and broader diversity of patients taking part in clinical trials to attain the most impactful results. For a clinical trial to succeed, accurate information must be available and able to flow between contract research organizations (CROs), sponsors, doctors, and potential patients. This is essential for raising awareness of trials, ensuring accessibility, facilitating recruitment, and maintaining retention.

Today, we can use RWD and cloud-based clinical applications to help determine trial feasibility, optimize site selection, and accelerate patient recruitment. Sponsors and CROs can select high-performing sites and understand qualified patient count, thus helping expedite recruitment. Meanwhile, integrating research data and information on clinical trials into electronic health records (EHRs) can make it easier for physicians to identify and enroll eligible patients for potential therapeutic options and trial opportunities.

We’re also advancing pharmacovigilance by coupling RWD and RWE with AI to help improve the detection of adverse drug effects, identify their causes, and reduce patient risk.

The goal is to create a continuously learning ecosystem that seamlessly connects research to point of care, and back again.

Open, continuously learning platform

Foundational to advancing RWD (and by default, the Learning Health Network) is the recently announced Oracle Health Data Intelligence platform. This open and continuously learning platform can unify data from thousands of sources to create longitudinal patient records. Health Data Intelligence helps transform vast data sets into actionable insights, bridges the gap between clinical research and care, and empowers stakeholders to make informed decisions across the entire healthcare continuum.

Through this platform, unified data from payers, healthcare providers, public health groups, and life sciences organizations is continually captured, analyzed, and made securely available to clinical and enterprise applications to inform next-best actions. Health Data Intelligence simplifies the process of preparing data for research and use, eliminating the need for specialized data science skills. Built-in analytics, powered by AI, can help spot trends and patterns, as well as generate new, evidence-based material that can aid in guiding clinical decisions.

For example, our insights engine identifies gaps in patient care and surfaces those back into provider workflows. The same could apply to the research-led generation of novel intelligence algorithms and models. New evidence, research findings, and emerging best practices can create immediately applicable content across entire health systems. This approach effectively resolves the disconnect between historically isolated research insights and clinical applications, thereby enhancing patient care.

Health Data Intelligence’s open APIs let a range of third-party developers plug their clinical applications into the platform. And because the platform runs on Oracle Cloud infrastructure, it’s designed with a security-first, zero-tolerance architecture.

Bringing clinical research into everyday patient care

The fusion of RWD, RWE, and cloud technology isn’t just a pathway to more efficient healthcare, but also a beacon of hope for patients awaiting breakthrough treatments. We can democratize research, enabling even small healthcare entities to engage in meaningful clinical studies. This broadens the scope of research and the equitable distribution of the benefits of clinical discoveries.

I envision a future where the silos between clinical research and care delivery are dismantled, and where every patient encounter and data point contribute to a collective learning system. It’s a future where personalized medicine exists at scale, informed by a rich tapestry of data and advanced computing.

Dr. Souza, a pediatric and emergency medicine doctor, is vice president and executive medical officer of Oracle Learning Health Network.